The Certified Data Engineering Training Course for Career Advancement at Oxford Training Centre is a rigorous, career-focused programme designed to prepare professionals for high-demand roles in data architecture, data infrastructure, and analytics engineering. As businesses across industries increasingly rely on big data to make strategic decisions, data engineers are tasked with building the robust data pipelines and platforms that enable data science and analytics operations. This course provides in-depth technical instruction, practical implementation, and real-world project experience to help professionals transition into or advance within the data engineering field.
This course stands firmly within the domain of IT and Computer Science Training courses, offering structured learning that encompasses the latest in cloud-based data engineering, distributed computing, data warehousing, and ETL processes. Participants will learn to design, develop, and manage large-scale data pipelines using industry-leading tools such as Python, SQL, Spark, Apache Airflow, Kafka, and cloud platforms including AWS, Azure, and Google Cloud Platform.
Whether you are starting out or seeking to upskill, this data engineering training course equips you with the core technical competencies and strategic mindset required to contribute meaningfully to enterprise data ecosystems. From certification courses for data engineers to full-stack data engineer training programs, this course is specifically designed for career advancement with data engineering skills.
Objectives
- Design and implement scalable data pipelines for both batch and real-time data processing.
- Understand the lifecycle of data from ingestion and transformation to storage and analytics.
- Deploy data workflows using ETL/ELT strategies with Apache Airflow and cloud-native orchestration tools.
- Build and optimize data lake and warehouse architectures for structured and unstructured datasets.
- Apply data modeling techniques for analytics, BI, and machine learning enablement.
- Master version control, testing, and CI/CD pipelines in a data engineering environment.
- Understand distributed computing concepts using Spark and Hadoop ecosystems.
- Work with cloud infrastructure for scalable and secure data storage and processing.
- Align learning outcomes with industry standards for data engineering certification and career pathways.
Target Audience
- IT professionals seeking a career-oriented data engineering certification to transition into data-focused roles.
- Aspiring engineers and developers looking for the best training course to advance a career in data engineering.
- Data analysts and scientists wanting to expand into engineering and infrastructure domains.
- Students and graduates seeking a structured data engineering course with real-world projects to start their careers.
- Individuals preparing for an industry-recognized data engineering certification.
- Mid-career professionals aiming to build backend data infrastructure for analytics and BI systems.
- Anyone researching how to become a certified data engineer through structured, hands-on learning.
How Will Attendees Benefit?
- Exposure to industry-validated tools and techniques in a real-world context through applied learning labs.
- Practical training in building data pipelines and platforms using cloud services, Spark, and orchestration frameworks.
- The ability to bridge engineering and analytical functions through effective data infrastructure management.
- A recognized certificate signaling readiness for training for data engineering jobs in enterprise environments.
- Hands-on projects that prepare learners for interviews, portfolio presentation, and on-the-job performance.
- Strategic career planning guidance tied to the best certification for a career in data engineering.
- Technical competence in version control, deployment automation, and modern data stack integration.
- Fluency in languages, platforms, and tools essential for modern data engineer training programs.
- Preparedness for hybrid and remote work models through mastery of data engineering bootcamp online modules.
Course Content
Module 1: Foundations of Data Engineering
- Overview of the data engineering role and responsibilities in modern enterprises
- Introduction to data systems: OLTP, OLAP, data lakes, and data warehouses
- Tools and platforms overview (Python, SQL, Linux, Git, APIs)
Module 2: Data Modeling and Database Management
- Relational and non-relational databases (PostgreSQL, MongoDB)
- Normalization, denormalization, and schema design principles
- Query optimization, indexing, and transaction management
Module 3: Data Ingestion and ETL Development
- Batch vs real-time ingestion strategies
- Building ETL/ELT pipelines using Apache Airflow
- Data extraction from APIs, files, and cloud data stores
Module 4: Distributed Computing with Spark
- Introduction to big data and the Hadoop ecosystem
- Data transformation and parallel processing using PySpark
- Real-world examples of Spark in analytics and data prep
Module 5: Data Infrastructure and Cloud Platforms
- Cloud platforms: AWS S3, Redshift, Azure Data Factory, Google BigQuery
- Building and maintaining cloud-based data lakes and warehouses
- Secure access, resource optimization, and role-based permissions
Module 6: Real-Time Data Processing and Streaming
- Kafka fundamentals and stream ingestion architecture
- Event-driven systems and message queuing techniques
- Designing systems for scalability, durability, and fault tolerance
Module 7: Testing, Version Control, and CI/CD
- Writing test cases for data workflows and validation logic
- Implementing Git-based version control for data projects
- Deploying CI/CD pipelines with Docker and Jenkins for data operations
Module 8: Capstone Project and Certification Preparation
- Final project involving the design and deployment of a production-ready data pipeline
- Documentation, presentation, and code review session
- Review for certified data engineering program with job focus assessment